PrivateLR: Differentially Private Regularized Logistic Regression

Version 1.2-21

PrivateLR implements two differentially private algorithms for
estimating L2-regularized logistic regression coefficients. A randomized
algorithm F is epsilon-differentially private (C. Dwork, Differential
Privacy, ICALP 2006), if
|log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
for any pair D, D' of datasets that differ in exactly one element, any
set S, and the randomness is taken over the choices F makes.